Abstract: We propose a co-part segmentation method that takes a set of point clouds of the same category as input where neither a ground truth label nor a prior network is required. With difficulties ...
Target Point Cloud Segmentation for Battery Swapping Robot Based on Multiscale Attention Aggregation
Abstract: Rapid and accurate segmentation of 3-D point clouds is critical for optimizing battery-swapping robots and ensuring precise assembly. To address the challenges of computational inefficiency ...
Abstract: Uncertainty estimation for point cloud semantic segmentation is to quantify the confidence degree for the predicted label of points, which is essential for decision-making tasks. This paper ...
Abstract: Accurate environmental perception is critical for autonomous vehicles, typically achieved through multi-sensor fusion. However, existing camera-radar fusion methods often neglect effective ...
Abstract: To address the limitations of insufficient geometric modeling and inadequate context fusion in indoor point cloud semantic segmentation, we propose Geometric-Relational Context Aggregation ...
The Java ecosystem has historically been blessed with great IDEs to work with, including NetBeans, Eclipse and IntelliJ from JetBrains. However, in recent years Microsoft's Visual Studio Code editor ...
The Transaction Aggregator ingests financial transaction data from three simulated sources, categorises each transaction using a rules engine with optional ML fallback, persists results to PostgreSQL, ...
Abstract: In this paper, a Backward Attentive Fusing Network with Local Aggregation Classifier (BAF-LAC) is proposed to improve the performance of 3D point cloud semantic segmentation. It consists of ...
Abstract: Multi-Point Dynamic Aggregation (MPDA) is a novel task model to determine task allocation for a multi-robot system. In an MPDA scenario, several robots with different abilities aim to ...
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